Image 5_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 3_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 6_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 4_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 1_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

Image 2_Transcriptomic profiling of diabetic retinopathy: insights into RPL11 and bisphenol A.jpeg by Jian Zhang (1682)

“…To validate the core gene, we conducted Gene Set Enrichment Analysis (GSEA, fgsea R package, version 1.35.8), immune cell infiltration profiling (CIBERSORT algorithm, version 1.03), molecular docking (AutoDock Vina, version 1.2.0), and molecular dynamics simulations (GROMACS, version 2022.4).…”

<b>Rethinking neighbourhood boundaries for urban planning: A data-driven framework for perception-based delineation</b> by Shubham Pawar (22471285)

“…</p><p dir="ltr"><b>Input:</b></p><ul><li><code>raw_data/glasgow_open_built/glasgow_open_built_areas.shp</code> - Grid defining sampling points</li></ul><p dir="ltr"><b>Command:</b></p><pre><pre>python svi_module/get_svi_data.py<br></pre></pre><p dir="ltr"><b>Output:</b></p><ul><li><code>svi_module/svi_data/svi_info.csv</code> - Image metadata (IDs, coordinates)</li><li><code>svi_module/svi_data/images/</code> - Downloaded street view images</li></ul><h3>Step 2: Predict Perceptions</h3><p dir="ltr">Use pre-trained deep learning models to predict perceptual qualities (safety, beauty, liveliness, etc.) from street view images.…”

DataSheet1_Identification of novel markers for neuroblastoma immunoclustering using machine learning.zip by Longguo Zhang (20105238)

“…Three machine learning algorithms (LASSO, SVM-RFE, and Random Forest) were used to screen biomarkers and synthesize their function in neuroblastoma.…”

Code and data for evaluating oil spill amount from text-form incident information by Yiming Liu (18823387)

“…These are separately stored in the folders “description” and “posts”.</p><h2>Algorithms for Evaluating Release Amount (RA)</h2><p dir="ltr">The algorithms are split into the following three notebooks based on their functions:</p><ol><li><b>"1_RA_extraction.ipynb"</b>:</li><li><ul><li>Identifies oil spill-related incidents from raw incident data.…”

Image 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.tif by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Table 3_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Table 1_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Table 2_Exploring the role of neutrophil extracellular traps in neuroblastoma: identification of molecular subtypes and prognostic implications.xlsx by Can Qi (540350)

“…Univariate Cox analysis and the LASSO algorithm were used to identify biomarkers for prognosis. …”

Spatiotemporal Soil Erosion Dataset for the Yarlung Tsangpo River Basin (1990–2100) by peng xin (21382394)

“…Bias correction was conducted using a 25-year baseline (1990–2014), with adjustments made monthly to correct for seasonal biases. The corrected bias functions were then applied to adjust the years (2020–2100) of daily rainfall data using the "ibicus" package, an open-source Python tool for bias adjustment and climate model evaluation. …”

Image 4_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Table 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Table 1_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Image 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Image 2_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.jpeg by Minhao Huang (4952764)

“…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”

Table 3_Integrated machine learning analysis of 30 cell death patterns identifies a novel prognostic signature in glioma.xlsx by Minhao Huang (4952764)

“…Through literature mining and GeneCards database screening, 30 programmed cell death (PCD)-related gene sets (total 11,681 genes) were curated, identifying 428 differentially expressed genes (DEGs; |log₂FC|>1, p < 0.05). A pan-death prognostic signature (Cell-Death Score, CDS) was constructed using 114 machine learning algorithm combinations, refined via CoxBoost to select 25 key genes. …”